A reconfigurable and magnetically responsive assembly for dynamic solar steam generation

Interfacial solar vapor generation is a promising technique to efficiently get fresh water from seawater or effluent. However, for the traditional static evaporation models, further performance improvement has encountered bottlenecks due to the lack of dynamic management and self-regulation on the evolving water movement and phase change in the evaporation process. Here, a reconfigurable and magnetically responsive evaporator with conic arrays is developed through the controllable and reversible assembly of graphene wrapped Fe3O4 nanoparticles. Different from the traditional structure-rigid evaporation architecture, the deformable and dynamic assemblies could reconfigure themselves both at macroscopic and microscopic scales in response to the variable magnetic field. Thus, the internal water transportation and external vapor diffusion are greatly promoted simultaneously, leading to a 23% higher evaporation rate than that of static counterparts. Further, well-designed hierarchical assembly and dynamic evaporation system can boost the evaporation rate to a record high level of 5.9 kg m−2 h−1. This proof-of-concept work demonstrates a new direction for development of high performance water evaporation system with the ability of dynamic reconfiguration and reassembly.


Supplementary Note 1. Derivation of the relationship between evaporation rate with surface temperature
For each point on the surface of the CA assembly, the heat inflow should be balanced with heat outflow, and as the solar thermal input for each point is the same, which can be described by, (1) where ∆ is the enthalpy of evaporation, and R is the evaporation rate. Thereafter, given that ∆ is nearly a constant in this temperature range, Supplementary equation (2) could be derived as follows: From this equation, it can be concluded that the cooler point has the higher evaporation rate owing to more heat inflow from the environment.

Supplementary Note 2. Derivation of the coupling holistic evaporation rate
At the bevel of the cone, the thermal conduction differential equation 1 can be expressed as, According to our proposed mechanism, the collective evaporation process can be divided into two consecutive processes: interfacial evaporation and water vapor diffusion. The interfacial evaporation rate could be described by Dalton's evaporation 2,3 equation1, given by where * is the saturated vapor pressure, and is the vapor pressure near over the surface, is the evaporation coefficient, equals 2.88 × 10 −7 • −1 here, which is obtained from the experiment. And water vapor diffusion rate could be described by Fick's first law, which is where is the normal unit vector. Therefore, the governing equations in this system could be given by and can be numerically solved by finite element method. In detail, the model is set to a cone with bottom radius = 2 mm, height = 5 mm, which is surrounded by cubic shaped damp air with side length = 30 mm. The relative humidity and temperature of the air boundary is set to 50% and 28℃ respectively.

Supplementary Note 3. Clarification for the relationship between stability of the macroscopic conic shape and the microscopic nanoparticles' rearrangement
As shown in Supplementary Figure 36, <r 2 > represents the mean square movement radius for the nanoparticles during rotation. For comparison, three situations were simulated including the rigid-body rotation, dynamic rotation with relaxation time of nanoparticles much shorter than that of the rotation and dynamic rotation with relaxation time of nanoparticles comparable to that of the rotation, which are labeled as rigid body, fast dynamic and slow dynamic rotation, respectively. It can be seen, for fast dynamic rotation, <r 2 > increases much faster than that of the rigid body, and reaches a stable value comparable to the highest level of the rigid body, indicating the rapid rearrangement of the nanoparticles in the CA assembly. However, for slow dynamic rotation, <r 2 > increases slowly, and reaches a much lower level than that of the stable rotation. Accordingly, the macroscopic conic shape for the fast dynamic rotation remains stable while that of the slow dynamic rotation contorts a lot and loses its stability as a conic assembly (Supplementary Figure 37). At the same time, the trajectory of four individual nanoparticles in these two conditions confirms that nanoparticles get no enough time in slow dynamic rotation, resulting in much lower degree of reconfiguration, as well as worse stability of the conic shape (Supplementary Figure 38). In experiments, the conic shape is quite stable even under rotation rate of 200 rpm, indicating its good stability. Therefore, in the CA assembly, the nanoparticles should undergo a fast reconfiguration process with relaxation time much shorter than that of the macroscopic rotation, which also accounts for stability of the conic shape.

Supplementary Note 4. Calculation flow and program construction of the Monte
Carlo simulation.
As shown in Supplementary Figure 35, according to the classic Monte Carlo method 4 , probability determined evolution is conducted. Firstly, the initial configuration (including coordinates and the dipole moments of particles) is set to a pre-relaxed tilted cone consisting of 2000 particles (the stable configuration in the initial external magnetic field). In each step, every particle in the system undergoes a possible configuration change process. Specifically, for each particle, a Monte Carlo step length is passed to a random direction which will change the total energy of the whole configuration. By comparing the energy of the new and current configuration, a transition state condition is judged to determine whether this particle movement could happen or not. After every particle accomplishing this process, the program changes the